Research on Classification and Recognition of Stem Sticks in Shredded Cut Tobacco Based on Hyperspectral Imaging
TAO Fazhan
YANG Dong
HONG Weiling
SU Ziqi
FU Zhumu
LIN Zhiping
Abstract：Regarding the classification,recognition,and detection of stem sticks in cut tobacco leaves,this article uses hyperspectral imaging technology and combines machine learning methods to classify and rapidly identify adulterated stem sticks in cut tobacco leaves.Firstly,based on shortwave near-infrared hyperspectral imaging technology,standard normal variate(SNV)transformation is applied to preprocess the spectral data of both cut tobacco leaves and stem sticks.The goal is to eliminate the effects of spectral scattering and reflectance,reducing various sources of interference.Subsequently,a feature wavelength selection is conducted using the successive projections algorithm(SPA),integrate the extreme gradient boosting(XGBoost)algorithm,proposed a stem stick classification model in shredded cut tobacco based on the XGBoost algorithm.Finally,a post-processing method is employed to achieve intelligent detection of stem sticks within cut tobacco leaves,combining the mean-shift mean-shift algorithm and morphological gradient algorithm.The classification results of the model are post-processed using this approach.The results demonstrate that the established SNV-SPA-XGBoost classification model achieves accuracy rates of 100%for the training set and 99.32%for the test set.After post-processing,the accuracy rates for detecting A1(1.0～1.5 cm),A2(0.5～1.0 cm),and A3(<0.5 cm)stem sticks reach 100%,95.50%,and 86%respectively.
Keywords：hyperspectral imagingmachine learningstem stickssuccessive projections algorithmXGBoostclassification
Publication Date：2024-06-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 32-42 )